Cache data processing method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a cache data processing method and device, equipment and a storage medium. The method is executed by a client, the client acquires computing node and local cache cluster information, and the method comprises the following steps: receiving a key value of target data in response to a data processing request, and determining a target slot position corresponding to the key value; determining a target node corresponding to the target slot position according to the target slot position, wherein the target node is a storage area in a local cache; and sendingthe target data on the distributed cache to a target node of a local cache according to the key value of the target data so as to process the target data. According to the embodiment of the invention,the target node of the local cache is determined, and the data of the distributed cache is sent to the local cache to be processed, so that the problems that the local cache is difficult to expand and the distributed cache is low in concurrency are solved, and the data processing efficiency is improved.

Description

technical field [0001] Embodiments of the present invention relate to Internet technologies, and in particular, to a cache data processing method, device, device, and storage medium. Background technique [0002] With the development of Internet technology and the information age, the amount of information is increasing rapidly, requiring more storage space to cache data, and the requirements for data storage and high concurrency are getting higher and higher. [0003] In the prior art, data is often called and processed directly from a local cache or a distributed cache. However, the capacity of the local cache is limited and cannot support the storage of large amounts of data, and the data will disappear with the restart of the application; although the distributed cache can be expanded, it will greatly increase the workload while adding nodes. The concurrency capability of QPS (Queries Per Second, the number of queries that can be processed per second) requires a large c...

AI Technical Summary

Problems solved by technology

However, the capacity of the local cache is limited and cannot support the storage of large amounts of data, and the data will disappear with the restart of the application; although the distributed cache can be expanded, it will greatly increase the workload while adding nodes. The concurrency capability of QPS (Queries Per Second, the number of queries that can be processed per second) requires a large cost, and it is difficult to process multiple requests in parallel at the same time, resulting in low processing efficiency of cached data

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